#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
宇宙意识生成器
从宇宙模型中提取和生成智慧认知
"""

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
from typing import Dict, List, Optional

class CosmicConsciousnessGenerator(nn.Layer):
    """
    宇宙意识生成器
    从宇宙状态中提取智慧模式
    """
    
    def __init__(self, 
                 cosmic_dim: int = 1024,
                 wisdom_dim: int = 512):
        """
        初始化宇宙意识生成器
        
        Args:
            cosmic_dim: 宇宙维度
            wisdom_dim: 智慧维度
        """
        super().__init__()
        
        self.cosmic_dim = cosmic_dim
        self.wisdom_dim = wisdom_dim
        
        # 宇宙真理提取网络
        self.truth_extractor = nn.Sequential(
            nn.Linear(cosmic_dim, cosmic_dim * 2),
            nn.GELU(),
            nn.Linear(cosmic_dim * 2, wisdom_dim),
            nn.Tanh()
        )
        
        # 智慧深度合成器
        self.wisdom_synthesizer = nn.TransformerEncoder(
            nn.TransformerEncoderLayer(
                d_model=wisdom_dim,
                nhead=8,
                dim_feedforward=2048,
                activation='gelu'
            ),
            num_layers=6
        )
        
        # 宇宙模式识别器
        self.pattern_recognizer = CosmicPatternRecognizer(wisdom_dim)
        
        # 意识焦点网络
        self.consciousness_focus = ConsciousnessFocusNetwork(wisdom_dim)
    
    def forward(self, cosmic_state: paddle.Tensor) -> Dict:
        """
        从宇宙状态生成智慧
        
        Args:
            cosmic_state: 宇宙状态向量
            
        Returns:
            包含智慧认知的字典
        """
        # 提取宇宙真理
        truth_embeddings = self.truth_extractor(cosmic_state)
        
        # 智慧深度合成
        wisdom_embeddings = self.wisdom_synthesizer(
            truth_embeddings.unsqueeze(1)
        ).squeeze(1)
        
        # 宇宙模式识别
        pattern_analysis = self.pattern_recognizer(wisdom_embeddings)
        
        # 意识焦点调整
        focused_wisdom = self.consciousness_focus(
            wisdom_embeddings, 
            pattern_analysis
        )
        
        return {
            'wisdom_embedding': focused_wisdom,
            'dominant_pattern': pattern_analysis['dominant_pattern'],
            'pattern_strength': pattern_analysis['pattern_strength'],
            'all_patterns': pattern_analysis['all_patterns'],
            'consciousness_clarity': pattern_analysis['clarity_score'],
            'truth_embeddings': truth_embeddings
        }

class CosmicPatternRecognizer(nn.Layer):
    """宇宙模式识别器"""
    
    def __init__(self, wisdom_dim: int):
        super().__init__()
        self.wisdom_dim = wisdom_dim
        
        # 宇宙基础模式库
        self.cosmic_patterns = self.initialize_cosmic_patterns()
        
        # 模式匹配网络
        self.pattern_matcher = nn.Linear(wisdom_dim, len(self.cosmic_patterns))
        
        # 模式强度评估
        self.strength_evaluator = nn.Sequential(
            nn.Linear(wisdom_dim, wisdom_dim // 2),
            nn.ReLU(),
            nn.Linear(wisdom_dim // 2, 1),
            nn.Sigmoid()
        )
    
    def initialize_cosmic_patterns(self) -> Dict[str, paddle.Tensor]:
        """初始化宇宙基础模式"""
        patterns = {
            'yin_yang': self.create_parameter(  # 阴阳动态平衡
                shape=[64, self.wisdom_dim],
                default_initializer=nn.initializer.Normal(std=0.1)
            ),
            'cyclical': self.create_parameter(  # 循环往复
                shape=[64, self.wisdom_dim], 
                default_initializer=nn.initializer.Uniform()
            ),
            'balance': self.create_parameter(   # 和谐平衡
                shape=[64, self.wisdom_dim],
                default_initializer=nn.initializer.Constant(0.5)
            ),
            'emergence': self.create_parameter( # 复杂性涌现
                shape=[64, self.wisdom_dim],
                default_initializer=nn.initializer.Normal(std=0.2)
            ),
            'unity': self.create_parameter(     # 万物一体
                shape=[64, self.wisdom_dim],
                default_initializer=nn.initializer.XavierUniform()
            )
        }
        return patterns
    
    def forward(self, wisdom_embeddings: paddle.Tensor) -> Dict:
        """识别宇宙模式"""
        # 计算与所有模式的相似度
        pattern_similarities = {}
        
        for pattern_name, pattern_vectors in self.cosmic_patterns.items():
            # 计算余弦相似度
            similarities = F.cosine_similarity(
                wisdom_embeddings.unsqueeze(1),
                pattern_vectors.unsqueeze(0),
                axis=-1
            )
            # 取最大相似度作为该模式强度
            pattern_similarities[pattern_name] = paddle.max(similarities)
        
        # 选择主导模式
        dominant_pattern = max(
            pattern_similarities.items(), 
            key=lambda x: x[1].item()
        )
        
        # 评估意识清晰度
        clarity_score = self.evaluate_clarity(pattern_similarities)
        
        return {
            'dominant_pattern': dominant_pattern[0],
            'pattern_strength': dominant_pattern[1].item(),
            'all_patterns': pattern_similarities,
            'clarity_score': clarity_score
        }
    
    def evaluate_clarity(self, pattern_similarities: Dict) -> float:
        """评估意识清晰度"""
        similarities = list(pattern_similarities.values())
        max_sim = max(similarities).item()
        second_max = sorted(similarities)[-2].item() if len(similarities) > 1 else 0
        
        # 清晰度 = 最大相似度 - 次大相似度
        clarity = max_sim - second_max
        return max(0.0, min(1.0, clarity))

class ConsciousnessFocusNetwork(nn.Layer):
    """意识焦点网络"""
    
    def __init__(self, wisdom_dim: int):
        super().__init__()
        self.wisdom_dim = wisdom_dim
        
        # 注意力机制
        self.attention = nn.MultiHeadAttention(
            embed_dim=wisdom_dim,
            num_heads=8,
            dropout=0.1
        )
        
        # 焦点调节器
        self.focus_adjuster = nn.Sequential(
            nn.Linear(wisdom_dim * 2, wisdom_dim),
            nn.GELU(),
            nn.Linear(wisdom_dim, wisdom_dim)
        )
    
    def forward(self, wisdom_embeddings: paddle.Tensor, 
                pattern_analysis: Dict) -> paddle.Tensor:
        """调整意识焦点"""
        # 自注意力机制增强重要特征
        attended_wisdom, attention_weights = self.attention(
            query=wisdom_embeddings.unsqueeze(1),
            key=wisdom_embeddings.unsqueeze(1),
            value=wisdom_embeddings.unsqueeze(1),
            return_attention_weights=True
        )
        
        attended_wisdom = attended_wisdom.squeeze(1)
        
        # 基于主导模式调整焦点
        dominant_strength = pattern_analysis['pattern_strength']
        focus_factor = dominant_strength
        
        # 焦点调节
        combined = paddle.concat([wisdom_embeddings, attended_wisdom], axis=-1)
        focused = self.focus_adjuster(combined)
        
        # 应用焦点因子
        final_wisdom = focus_factor * focused + (1 - focus_factor) * wisdom_embeddings
        
        return F.normalize(final_wisdom)

class UniversalWisdomLibrary:
    """宇宙智慧库"""
    
    def __init__(self):
        self.wisdom_templates = self.initialize_wisdom_templates()
    
    def initialize_wisdom_templates(self) -> Dict[str, List[str]]:
        """初始化智慧模板"""
        return {
            'yin_yang': [
                "阴阳互动，生生不息。对立中见统一，变化中见永恒。",
                "阳主动而阴主静，两者相生相克，构成宇宙的动态平衡。",
                "在极阳中寻找阴的种子，在极阴中发现阳的萌芽。"
            ],
            'cyclical': [
                "宇宙如环无端，循环往复。春生夏长，秋收冬藏。",
                "盛极而衰，否极泰来。循环是宇宙的基本节律。", 
                "终点即是起点，死亡孕育新生。"
            ],
            'balance': [
                "中庸之道，过犹不及。平衡是宇宙的健康状态。",
                "在动与静、进与退之间找到黄金中点。",
                "真正的力量来自平衡，而非极端。"
            ],
            'emergence': [
                "从简单中涌现复杂，从混沌中产生秩序。",
                "整体大于部分之和，模式从互动中诞生。",
                "创造性在边缘地带涌现，在临界点上突破。"
            ],
            'unity': [
                "万物一体，互联互通。个体是整体的缩影。",
                "分离是表象，连接是本质。我们都是宇宙的一部分。",
                "在一粒沙中看见世界，在一朵花中窥见天堂。"
            ]
        }
    
    def get_wisdom_by_pattern(self, pattern: str, strength: float) -> str:
        """根据模式获取智慧语句"""
        if pattern not in self.wisdom_templates:
            return "宇宙的智慧深不可测，需要静心体会。"
        
        templates = self.wisdom_templates[pattern]
        
        # 根据强度选择模板深度
        if strength > 0.8:
            template_index = 2  # 最深层的智慧
        elif strength > 0.6:
            template_index = 1  # 中等深度
        else:
            template_index = 0  # 基础智慧
        
        return templates[template_index]

class CreativeWisdomGenerator(nn.Layer):
    """创造性智慧生成器"""
    
    def __init__(self, wisdom_dim: int):
        super().__init__()
        self.wisdom_dim = wisdom_dim
        
        # 创造性变换网络
        self.creative_transform = nn.Sequential(
            nn.Linear(wisdom_dim, wisdom_dim * 2),
            nn.GELU(),
            nn.Dropout(0.1),
            nn.Linear(wisdom_dim * 2, wisdom_dim),
            nn.Tanh()
        )
        
        # 新颖性评估
        self.novelty_evaluator = nn.Linear(wisdom_dim, 1)
    
    def forward(self, wisdom_embedding: paddle.Tensor) -> Dict:
        """生成创造性智慧"""
        # 创造性变换
        creative_wisdom = self.creative_transform(wisdom_embedding)
        
        # 评估新颖性
        novelty_score = F.sigmoid(self.novelty_evaluator(creative_wisdom))
        
        return {
            'creative_wisdom': creative_wisdom,
            'novelty_score': novelty_score.item(),
            'creativity_level': self.assess_creativity_level(novelty_score.item())
        }
    
    def assess_creativity_level(self, novelty_score: float) -> str:
        """评估创造力水平"""
        if novelty_score > 0.9:
            return "突破性创造"
        elif novelty_score > 0.7:
            return "高度创新" 
        elif novelty_score > 0.5:
            return "适度创新"
        else:
            return "常规思维"